Association between influenza infection and cardiovascular diseases: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives Influenza infection may increase the risk of cardiovascular diseases (CVDs), but the extent of this link is uncertain. This systematic review and meta-analysis aimed to quantify the association between influenza infection and CVDs. Methods We conducted a comprehensive search of major databases from inception to 2024, identifying studies that investigated the association between influenza infection and CVDs. Eligible studies included cohort, case–control, and randomized controlled trials reporting on cardiovascular outcomes (acute CVDs) following influenza infection or risk of influenza infection in CVD patients (chronic CVDs). Data were extracted and pooled using random-effects models, and heterogeneity was assessed using the I 2 statistic. Results A total of 11 studies (15 datasets) involving 7327 participants were included in the meta-analysis. Overall, influenza infection was significantly associated with CVDs based on 10 datasets (odds ratio (OR) = 1.76, 95% confidence interval (CI): 1.02–3.03). However, the analysis of the five datasets indicated no significant association between pre-existing CVDs and an increased risk of influenza infection (OR = 0.91, 95% CI: 0.80–1.03). Subgroup analyses and meta-regression highlighted that study quality and design could significantly influence the risk of developing CVDs among patients with influenza. Conclusions This meta-analysis provides quantitative evidence that influenza infection could be a potential risk factor for subsequent cardiovascular events. These findings emphasize the need for preventive measures, including vaccination, especially in high-risk populations. Further research is needed to explore the underlying mechanisms and impact of influenza on cardiovascular outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".